Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncoding RNAs (lncRNAs) and machine learning (ML) could help predict COVID-19 severity. Goals/Aims: To develop a model based on lncRNAs and ML for predicting COVID-19 severity. Methods/Approach: Expression data of 2906 lncRNAs were obtained by targeted sequencing in plasma samples collected at baseline from four independent cohorts, totaling 564 COVID-19 patients. Patients were aged 18+ and were recruited from 2020 to 2023 in the PrediCOVID cohort (n=162; Luxembourg), the COVID19_OMICS-COVIRNA cohort (n=100, Italy), the TOCOVID cohort (n=233, Spain), and the MiRCOVID cohort (n=69, Germany). The study complied with the Declaration of Helsinki. Cohorts were approved by ethics committees and patients signed an informed consent. Results/Data: After data curation and pre-processing, 463 complete datasets were included in further analysis, representing 101 severe patients (in-hospital death or ICU admission) and 362 stable patients (no hospital admission or hospital admission but not ICU). Feature selection with Boruta, a random forest-based method, identified age and five lncRNAs (LINC01088-201, FGDP-AS1, LINC01088-209, AKAP13, and a novel lncRNA) associated with disease severity, which were used to build predictive models using six ML algorithms. A naïve Bayes model based on age and five lncRNAs predicted disease severity with an AUC of 0.875 [0.868-0.881] and an accuracy of 0.783 [0.775-0.791]. Conclusion: We developed a ML model including age and five lncRNAs predicting COVID-19 severity. This model could help improve patients’ management and cardiovascular outcomes.

This research was focused on testing two water filters - Brita and Profissimo, which were filtering two and five liters of water every day. The lifespan of used filters is four weeks, while they have been actively used for eight weeks in this study to check for their efficiency after exceeded usage. Along with this, the quality of tap water, which was filtered using these two types of filters, was also tested. The experiment of the whole study was divided into three main stages: microbiological analysis, biochemical analysis, and UV-VIS spectrophotometric analysis of filtered water. The measurements were done every five days. The aim was to compare the performances of Brita and Profissimo filters after the completion of the required experiments. Based on the results that are obtained from all the analyses mentioned previously, we can conclude that Brita 2l filter was the most efficient, while Profissimo 5l filter appeared to be the least effective filter.  It is important to emphasize that the tap water in Sarajevo is generally clean and drinkable, so there is a possibility that when using more polluted water, greater deviations in the operation of filters can be observed. Overall, both water filters were usable even after two months of active usage and our measurements showed good water quality which lacks impurities and is safe for drinking.

Protein structure prediction is an important process that carries a lot of benefits for various areas of science and industry. Template modeling is the most reliable and most popular method, depending on the solved structures from the Protein Data Bank. An important part of it is template selection, using different methods, which is a challenging task that requires special attention because the proper selection of protein template can lead to a more accurate protein prediction. This study focuses on the relationships between predicted proteins, taken from the Swiss-model repository, and their templates, on a larger scale. Features of predicted proteins are taken into account, including protein length, sequence identity, and sequence coverage. Quality assessment scores are compared and analyzed between the predicted proteins and their templates. Overall, quality assessment scores of predicted proteins show a moderate positive correlation to the sequence identity with the templates. Moreover, based on our data, the level of template quality is noticeably correlated with the predicted protein structuers, because templates with higher quality scores will, on average, also allow for the modeling of predicted proteins with higher quality scores.

Proteins are in the focus of research due to their importance as biological catalysts in various cellular processes and diseases. Since the experimental study of proteins is time-consuming and expensive, in silico prediction and analysis of proteins is common. Template-based prediction is the most reliable, which is why the aim of this study is to analyze how important are the primary features of proteins for their quality score. Statistical analysis shows that protein models with a resolution lower than 3 A or R value lower than 0.25 have higher quality scores when compared individually to their counterparts. Machine learning algorithm random forest analysis also shows resolution to have the highest importance, while other features have lower but moderate importance scores. The exception is the presence of ligand in protein models, which does not have an effect on the global protein quality scores, both through statistical and machine learning analyses.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

In order to define the term GMO, different scientific definitions and legal explanations are available. In the regulation process of GM foods, the US and EU legal frameworks are based on the methodologies themselves. Currently, for the production of GMOs, several genome editing tools are available. Along with different site-directed nucleases (ZFN, TALENs, etc.), RNAi and CRISPR/Cas9 have proven to be the very effective tools for genome editing. According to the current EU legislative, introduced in 2018, CRISPR/Cas9 and RNAi techniques are regulated as methods that produce GMOs, because the methodology of the process itself resembles the traditional breeding methods. In the past few years, a large number of scientific publications have confirmed that CRISPR/Cas9 and RNAi technology produce GMOs, supporting and suggesting that the legislation policies in the EU and especially in the USA have to be elaborated. Besides, a huge public pressure makes it difficult to develop and implement new methodologies for GMO production. For this reason, ELSI society is responsible to investigate and question whether the new genetic engineering techniques produce GMO food that is safe for human consumption.

© The Author 2020. Published by ARDA. Abstract Clean water is essential to our existence and problems might arise when it becomes contaminated with different pathogens, which might pose a threat to human health. Tap water is generally considered drinkable since it passes different forms of disinfection during processing. Some households have additional disinfection procedures, the most common one being the usage of charcoal filters, in order to further clean the tap water from both undesirable solvents and microorganisms. In the first independent study of this kind, we have tested tap water for bacteria from five different locations in Sarajevo, and we have tested the efficiency of charcoal filter in trapping of bacteria. According to regulations in Bosnia and Herzegovina, there should be 1 colony forming unit (CFU) per 50ul of water sample, which was satisfied in only one location from Sarajevo, while one had significantly higher levels (6.7, p val. 0.0148). Overall, the charcoal filter has decreased the number of bacteria in the water, with the exception of one sample.